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All Journal Ambidextrous : Journal of Innovation, Efficiency and Technology in Organization
Dede Brahma Arianto
Faletehan University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Classification of Symptoms of Disease in Early Childhood Using the Decision Tree Algorithm Nissa Albantaniyah; Dede Brahma Arianto
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 4 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v4i02.494

Abstract

Diseases in early childhood often have similar symptoms, making it difficult to process early diagnosis. This study aims to classify disease symptoms in early childhood using the Decision Tree algorithm. The data used is in the form of child health symptom data which is processed through the pre-processing stage and divided into training data and testing data. The results of the study show that the Decision Tree algorithm is able to classify disease symptoms well and can help the early diagnosis process faster and more systematically. The results of the evaluation show that the implementation of immunization of school children in various regions has quite good achievements, with the percentage of immunization coverage in the range of 66% to more than 90%. This high percentage shows that most children have successfully received immunizations in accordance with the set targets, so that the immunization program can be said to be running consistently and effectively.
Implementation of Random Forest Algorithm to Determine Food Allergies Ratu Aisyah; Dede Brahma Arianto
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 4 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v4i02.496

Abstract

Food allergy diagnosis faces challenges due to symptom variation and delays in conventional testing. This study aims to classify food allergy types using the Random Forest algorithm on patient data including age, gender, food type, symptoms, and severity. A quantitative experimental design was implemented with a secondary dataset of 1,000 medical records as the population, divided through stratified sampling (train-test ratio 80:20). Data preprocessing included label encoding of categorical variables, followed by supervised classification analysis using Python scikit-learn. The results showed a model accuracy of 85%, precision of 84%, recall of 86%, and F1-score of 85%, indicating strong performance with a balanced error rate in the validation confusion matrix. In conclusion, Random Forest effectively supports the rapid identification of food allergies, potentially serving as a clinical decision-making tool with the need for larger prospective datasets.
Predicting the Risk of Hypertension in Adult Patients Using the Random Forest Algorithm Santinah; Dede Brahma Arianto
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 4 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v4i02.497

Abstract

Hypertension remains a persistent and widespread health problem in the adult population, yet many cases go undetected due to limited early symptoms and reliance on conventional clinical assessment. This study aims to develop and evaluate a hypertension risk prediction model in adult patients using the Random Forest algorithm. This study employed a quantitative approach with an exploratory–predictive study design based on electronic secondary data, with a descriptive–analytical framework utilizing data mining techniques. The study population comprised all adult patients registered at selected healthcare facilities, while the sample consisted of 120 adult patients selected by purposive sampling from the hypertension risk dataset on Kaggle. The instrument used was a structured electronic medical record table, including age, gender, body mass index (BMI), blood pressure, and relevant medical history. The data underwent preprocessing and encoding, then were analyzed using the Random Forest algorithm on the Python platform with the scikitlearn library. Model performance was evaluated using accuracy, precision, recall, and F1score metrics. The results showed that the Random Forest model provided an accuracy of 87.5%, precision of 91.7%, recall of 84.6%, and F1 score of 88.0%, indicating a strong hypertension risk classification capability. The study concluded that Random Forest can be utilized as a reliable decision support system for early detection of hypertension risk in adult populations, especially when integrated with electronic medical records.
Application of Logistic Regression Method for Predicting Diabetes Mellitus Dendi Pratama Riawan; Dede Brahma Arianto
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 4 No. 03 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v4i02.539

Abstract

Diabetes is a chronic disease that requires early detection to prevent complications. This study refers to the analysis of diabetes prediction using the Logistic Regression algorithm. The data used comes from the open dataset platform, namely Kaggle, including health attributes such as Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Age, Outcome. The process in this study includes data cleaning, model development, and prediction. Model assessment was carried out using Confusion Matrix to calculate accuracy, Precision, Recall, and F1-Score, which is supported by ROC Curve analysis. The findings in this study show that the Logistic Regression model achieved an accuracy level of 75.32% and an AUC of 0.8232, indicating that the classification performance is quite good in predicting diabetes conditions
Co-Authors Dendi Pratama Riawan Nissa Albantaniyah Ratu Aisyah Santinah